Architecture Decision Records

Architecture Decision Records

Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.

When to Use This Skill

• Making significant architectural decisions
• Documenting technology choices
• Recording design trade-offs
• Onboarding new team members
• Reviewing historical decisions
• Establishing decision-making processes

Core Concepts

1. What is an ADR?

An Architecture Decision Record captures:

• Context: Why we needed to make a decision
• Decision: What we decided
• Consequences: What happens as a result

2. When to Write an ADR

3. ADR Lifecycle

Proposed → Accepted → Deprecated → Superseded
              ↓
           Rejected

• Proposed: Under discussion
• Accepted: Decision made, implementing
• Deprecated: No longer relevant
• Superseded: Replaced by another ADR
• Rejected: Considered but not adopted

Template

# ADR-0001: Use PostgreSQL as Primary Database

- Status:
- Date:
- Deciders:
- Consulted:
- Informed:

## Decision Outcome

One paragraph summary of the decision and its rationale.

## Context and Problem Statement

We need to select a primary database for our new e-commerce platform. The system
will handle:

- ~10,000 concurrent users
- Complex product catalog with hierarchical categories
- Transaction processing for orders and payments
- Full-text search for products
- Geospatial queries for store locator

The team has experience with MySQL, PostgreSQL, and MongoDB. We need ACID
compliance for financial transactions.

## Decision Drivers

- **Must have ACID compliance** for payment processing
- **Must support complex queries** for reporting
- **Should support full-text search** to reduce infrastructure complexity
- **Should have good JSON support** for flexible product attributes
- **Team familiarity** reduces onboarding time

## Considered Options

### Option 1: PostgreSQL

- **Pros**: ACID compliant, excellent JSON support (JSONB), built-in full-text
  search, PostGIS for geospatial, team has experience
- **Cons**: Slightly more complex replication setup than MySQL

### Option 2: MySQL

- **Pros**: Very familiar to team, simple replication, large community
- **Cons**: Weaker JSON support, no built-in full-text search (need
  Elasticsearch), no geospatial without extensions

### Option 3: MongoDB

- **Pros**: Flexible schema, native JSON, horizontal scaling
- **Cons**: No ACID for multi-document transactions (at decision time), team has
  limited experience, requires schema design discipline

## Decision

We will use **PostgreSQL 15** as our primary database.

## Rationale

PostgreSQL provides the best balance of:

1. **ACID compliance** essential for e-commerce transactions
2. **Built-in capabilities** (full-text search, JSONB, PostGIS) reduce
   infrastructure complexity
3. **Team familiarity** with SQL databases reduces learning curve
4. **Mature ecosystem** with excellent tooling and community support

The slight complexity in replication is outweighed by the reduction in
additional services (no separate Elasticsearch needed).

## Consequences

### Positive

- Single database handles transactions, search, and geospatial queries
- Reduced operational complexity (fewer services to manage)
- Strong consistency guarantees for financial data
- Team can leverage existing SQL expertise

### Negative

- Need to learn PostgreSQL-specific features (JSONB, full-text search syntax)
- Vertical scaling limits may require read replicas sooner
- Some team members need PostgreSQL-specific training

### Risks

- Full-text search may not scale as well as dedicated search engines
- Mitigation: Design for potential Elasticsearch addition if needed

## Implementation Notes

- Use JSONB for flexible product attributes
- Implement connection pooling with PgBouncer
- Set up streaming replication for read replicas
- Use pg_trgm extension for fuzzy search

## Related Decisions

- ADR-0002: Caching Strategy (Redis) - complements database choice
- ADR-0005: Search Architecture - may supersede if Elasticsearch needed

## References

- [PostgreSQL JSON Documentation](https://www.postgresql.org/docs/current/datatype-json.html)
- [PostgreSQL Full Text Search](https://www.postgresql.org/docs/current/textsearch.html)
- Internal: Performance benchmarks in `/docs/benchmarks/database-comparison.md`

Review Process

## ADR Review Checklist

### Before Submission

- [ ] Context clearly explains the problem
- [ ] All viable options considered
- [ ] Pros/cons balanced and honest
- [ ] Consequences (positive and negative) documented
- [ ] Related ADRs linked

### During Review

- [ ] At least 2 senior engineers reviewed
- [ ] Affected teams consulted
- [ ] Security implications considered
- [ ] Cost implications documented
- [ ] Reversibility assessed

### After Acceptance

- [ ] ADR index updated
- [ ] Team notified
- [ ] Implementation tickets created
- [ ] Related documentation updated

Best Practices

Do's

• Write ADRs early - Before implementation starts
• Keep them short - 1-2 pages maximum
• Be honest about trade-offs - Include real cons
• Link related decisions - Build decision graph
• Update status - Deprecate when superseded

Don'ts

• Don't change accepted ADRs - Write new ones to supersede
• Don't skip context - Future readers need background
• Don't hide failures - Rejected decisions are valuable
• Don't be vague - Specific decisions, specific consequences
• Don't forget implementation - ADR without action is waste